Our approach is to generate explanations in the form of multistep entailment trees, such as shown in Figure REF ,
made up of individual textual entailment (TE) steps [1]}.
Unfortunately, although there are several single-step entailment datasets available
[2]}, [3]} no dataset of multistep entailments exists,
and so a significant contribution of this paper is the construction
of such a dataset, called EntailmentBank. EntailmentBank contains
1,513  multistep entailment trees for accompanying QA pairs, constructed using expert annotators,
and is the first dataset of its kind. We then define three increasingly difficult explanation
tasks over this dataset, namely: generate a valid entailment tree for a given QA pair
given (a) all relevant sentences (the leaves of the gold entailment tree), (b) all
relevant and some distractor sentences, or (c) a full corpus.
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